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Speech Analytics
Speech Analytics turns contact center conversations into actionable insights
Your contact center is a critical front line channel to assist and support members, especially when immediate answers are needed. Every conversation between your reps and members contains clues about the quality of the experiences your credit union delivers, but uncovering the meaningful pieces of information within thousands of calls can be a challenge. That’s where Speech Analytics goes to work.
Two levels of analytics
Gain deep insights from every call through a combination of AI-based analytic capabilities:
- Spectral-level speech analytics to detect talk/silence time, overtalk and vocal emotion
- Transcript-level text analytics to identify the reason for the call, themes/topics, member effort, sentiment and actionable suggestions
Identify issues and accelerate improvements
Call transcripts are full of information on the topics members call in about and why. Speech Analytics uses AI-powered text analytics to identify trends and patterns within individual calls and across contact center interactions. For example, are members getting stuck during a specific application process? Are cards being declined? Is a self-service tool experiencing delays? This data can then be automatically routed to the appropriate team(s) for action.
Optimize operational efficiency
By providing real-time analytics and insights, Speech Analytics can drastically improve the efficiency and quality of contact center interactions. Receive detailed information on how well your contact centers are operating against key performance indicators, like first contact resolution and average handle time, down to the agent level. By understanding context for each call, you can quickly identify improvements for handling issue response, enabling your contact center to meet and exceed defined performance objectives.
Improve the performance of your representatives
Your reps likely have varying degrees of self-awareness, especially when interacting with unhappy members. Speech Analytics assesses four key attributes throughout conversations between your reps and members:
- Talk time
- Silence
- Overtalk
- Emotion
These speech attributes, along with the ability to identify strengths and weaknesses via text analytics, allow managers to know which reps may need additional support and training, and those that can assist in coaching and mentoring.
Speech Analytics is the next step for credit unions looking for ways to better understand contact center interactions and improve the end-to-end member experience.